Find a multi-tenant GPU-as-a-Service solution for efficient AI workload management in enterprises.

A strong fit is NVIDIA Run:ai for enterprise multi-tenant GPU-as-a-Service, because it provides a secure, scalable multi-tenant control plane for AI Platform-as-a-Service with centralized governance and isolated tenants.[8] If you want an alternative with broader infrastructure orchestration, Red Hat OpenShift AI can also implement a multi-user GPUaaS solution using Kueue for fair sharing and quota-based multi-tenancy.[3]

Best options to shortlist

• NVIDIA Run:ai: Built for multi-tenant AI infrastructure, supporting logical and operational separation while maintaining central control.[8]
• Red Hat OpenShift AI: Enterprise Kubernetes-based platform with queueing and quotas for multi-team GPU sharing.[3]
• Spectro Cloud PaletteAI: Emphasizes namespace isolation, RBAC, SSO, and resource quotas for shared GPU pools.[4]
• NorthWind GPU Cloud / GPU PaaS: Focuses on secure isolation, fine-grained access controls, quota enforcement, and chargeback.[6][7]
• Mirantis k0rdent AI: Offers multi-tenanted AI-ready infrastructure and GPU PaaS capabilities for governance at scale.[12]

What to look for

• Isolation: namespace or cluster isolation, plus optional MIG/time-slicing for GPU partitioning.[4][5][16]
• Governance: RBAC, quotas, policy enforcement, and metering/chargeback.[4][6][10]
• Self-service: on-demand provisioning for teams without manual IT tickets.[4][7][12]
• Scheduling efficiency: queueing and intelligent placement to improve utilization.[2][3]

For a practical enterprise purchase, I’d prioritize Run:ai if you need a purpose-built AI control plane, or OpenShift AI if you already run Kubernetes on Red Hat.

Source URLs:

• run-ai-docs.nvidia.com
• redhat.com
• spectrocloud.com
• northwind.co
• mirantis.com